Channel synchronization based on deep learning

Abstract

AbstractAiming at the difficulty of the deep neural network (DNN) adapting to channel changes in communication systems, a channel synchronization deep neural network (CSDNN) based on deep learning (DL) is designed for realizing carrier synchronization, bit timing synchronization, and automatic gain control (AGC). By introducing a frequency‐domain cyclic convolution (FDCC) layer, the network transformed the time‐domain triangle activation into frequency domain linear activation taking FFT and IFFT matrixes as the activation function, solved the reverse gradient transmission‐blocking problems in training the time‐domain carrier synchronization neural network, effectively overcome the FFT inherent “fence” effect, and accurately compensated carrier frequency offset; By introducing a time‐domain cyclic convolution (TDCC) layer and the special frame structure design containing repetitive training sequence, the network training was completed to realize bit timing synchronization under the condition of the uncertain corresponding relationship between training data and labels. Combining phase inverse rotation dense (PIRD) layer, the network can be trained with very little training data to complete fast carrier synchronization and timing synchronization, at the same time adjust the received signal gain and suppress the jamming, which makes it is possible to train the channel synchronization deep neural network online under jamming environment, and provide a feasible way of realizing the intelligent communication system.